Systems and methods for predicting failure of electronic systems and assessing level of degradation and remaining useful life

ABSTRACT

Disclosed are systems and methods for prognostic health management (PHM) of electronic systems. Such systems and methods present challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. The systems and methods are directed to the health monitoring and failure prediction of electronic systems, including the diagnostic methods employed to assess current health state and prognostic methods for the prediction of electronic system failures and remaining useful life. The disclosed methodologies include three techniques: (1) use of existing electronic systems data (circuit as a sensor); (2) use of available external measurements as condition indicators and degradation assessor; and (3) performance assessment metrics derived from available external measurements.

This application claims priority from U.S. 60/826,426 for “Diagnosticsand Prognostics for Prediction of Electronic System Failures and UsefulLife Remaining,” filed Sep. 21, 2006, which is hereby incorporated byreference in its entirety.

GOVERNMENT RIGHTS

These inventions were made with Government support under the SmallBusiness Innovative Research program (contracts #N68335-06-C-0080,#N68335-04-C-0093, #N68335-05-C-0099, #N68335-07-C-0170, and#N68335-05-C-0128) awarded by Naval Air Warfare Center and Joint StrikeFighter program office. The Government has certain rights in theinventions.

The disclosed system and method is directed to the health monitoring andfailure prediction of electronic systems, including the diagnosticmethods employed to assess current health state and prognostic methodsfor the prediction of electronic system failures and remaining usefullife. The disclosed technology utilizes three core techniques: (1) Useof existing electronic systems data (Circuit as a Sensor); (2) Use ofavailable external measurements as condition indicators and degradationassessor; and (3) Performance assessment metrics derived from availableexternal measurements.

BACKGROUND AND SUMMARY

With increased reliance on the operation of electronic equipment forday-to-day tasks, as well as the circuits and components within them, itis increasingly important to be able to assess not only the operatingstate of such equipment, but also if and when such equipment isexperiencing degraded operation or is near failure and end of usefullife. The ability to have Condition Based Maintenance (CBM) andPrognostic Health Management (PHM) capability on electronic systems, inorder to monitor operating states, track performance, identify degradedperformance and predict useful life is of significant advantage to themilitary as well as the commercial sector.

The electronic systems PHM technology begins by utilizing bothdiagnostic and prognostic features to develop health indicators toassess the current health and predict the amount of useful liferemaining of an electronic system.

An electronic health indicator is a collection of one or more diagnosticfeatures used to determine the overall lifetime (or health) of a system.An electronic health indicator is primarily used to determine thepercentage of health remaining, or health index of a system.

A prognostic feature is a collection of one or more diagnostic featuresused to measure the rate of degradation to predict the amount of timeleft remaining during the useful life of the system, also referred to asRemaining Useful Life (RUL). One aspect of the present invention is thenovel approach employed, particularly including the following: (i) Noexternal circuit requirements; (ii) No circuit or system alterations;(iii) Data acquisition using low bandwidth connection; (iv) No externalsensor requirements; and (v) Identification and verification of features(feature extraction) as trend indicators of damage accumulation.

Prognostic health management using minimal or no sensors is a furtheradvantage as it avoids increasing costs and reduces the complexity ofthe equipment. Accordingly, aspects of the disclosed systems and methodsare directed to the use of PHM techniques both at a general equipmentlevel and also at the electronic system component and circuit level.

Use of existing electronic systems data (Circuit as a Sensor): Thedisclosed embodiments address the need for diagnostics and prognosticsby providing a method to diagnose and predict electronic system failuresand provide information supporting remaining useful life (RUL)assessment and prediction. This method incorporates existing data,typically utilized to perform a core or required device operation andnot originally designed for failure prediction, to provide aself-contained system to detect faults and predict failures. As usedherein, this is referred to as “circuit as sensor” (CAS). The circuit assensor concept, enables implementation of prognostics for electronicdevices, including devices having analog and/or digital components andin particular those that are digital and radio frequency in nature,utilizing few, if any, prognostics dedicated sensors. Examples of suchdevices would include, but are not limited to RF, IF, and basebandcircuits, various digital circuits and motor drive applications andactuator controllers, as well as digital circuit error checking and flowcontrol. This technology is presented using two use cases: a globalpositioning system (GPS) receiver and a RF transreceiver integratedcircuit.

Use of available external measurements as condition indicators anddegradation assessor: The approach integrates collaborative diagnosticand prognostic techniques from engineering disciplines includingstatistical reliability modeling, damage accumulation models,physics-of-failure modeling, signal processing and feature extraction,and automated reasoning algorithms. Further disclosed in embodimentsherein is a PHM system for monitoring performance of an electronicsystem, comprising: a plurality of electronic circuit components (e.g.,MOSFET), each component having a modeled operating state relative to atleast one feature and each generating respective signals representativeof the feature pursuant to the component operation; a data collectionmemory (e.g., RS-232 buffer, laptop) for storing samples of saidelectronic signals; and a computer (laptop), responsive to saidelectronic signals and the modeled operating state, for performing dataanalysis relative to the feature and detecting a variance in theoperation of the component, wherein the computer further determines thehealth and/or remaining useful life of the component and the electronicsystem.

Performance assessment metrics derived from available externalmeasurements: This method uses model-based assessments in the absence offault indications, and updates the model-based assessments with sensedinformation when it becomes available to provide health state awarenessat any point in time. Intelligent fusion of this diagnostic informationwith historical component reliability statistics provides a robusthealth state awareness as the basis for accurate prognostic predictions.

The following patents are believed to provide examples related toelectronic prognostics and are hereby incorporated by reference, intheir entirety, for their teachings:

7,034,660 Sensor devices for structural Apr. 03, 2002 health monitoring6,892,317 Systems and methods for failure Dec. 16, 1999 prediction,diagnosis and remediation using data acquisition and feedback for adistributed electronic system 6,807,507 Electrical over stress (EOS)Jun. 27, 2002 monitor 6,782,345 Systems and methods for Oct. 03, 2000diagnosing electronic systems 6,747,445 Stress migration test struc-Oct. 31, 2001 ture and method therefore 6,745,151 Remote diagnostics andprog- May 16, 2002 nostics methods for complex systems 6,529,135Integrated electric motor Oct. 12, 1999 monitor 6,363,332 Method andapparatus for pre- Dec. 22, 1998 dicting a fault condition usingnon-linear curve fitting techniques 5,719,495 Apparatus forsemiconductor Jun. 05, 1996 device fabrication diagnosis and prognosis5,270,222 Method and apparatus for Dec. 31, 1990 semiconductor devicefabrication diagnosis and prognosis

The following papers also described the use of electronic prognosticsand prognostic health management techniques and methods, and are herebyincorporated by reference in their entirety:

-   Brown, D. W.; Kalgren, P. W.; Byington, C. S.; Orsagh, R. F.;    Electronic prognostics—A case study using Global Positioning System    (GPS), Autotestcon 2005, IEEE Systems Readiness Technology    Conference, September 2005;-   Brown, D. W.; Kalgren, P. W.; Roemer M.; Dabney, T.; Electronic    Prognostics a Case Study Using Switched Mode Power Supplies (SMPS),    Autotestcon 2006, IEEE Systems Readiness Technology Conference,    September 2006;-   Ginart, A; Brown, D; Kalgren, P; and Roemer, M; On-line Ringing    Characterization as a PHM Technique for Power Drives and Electrical    Machinery, Autotestcon 2007, IEEE Systems Readiness Technology    Conference, Baltimore's Inner Harbor, Baltimore Md., Sep. 17-20,    2007;-   Kalgren, P.; Baybutt, M.; Minnella, C.; Ginart, A.; Roemer, M.;    Dabney, T.; Application of Prognostic Health Management in Digital    Electronic Systems, Big Sky, Mont., Mar. 3-10, 2007; and-   Nanduri, S.; Almeida P.; Kalgren, P.; Roemer, M.; Circuit as a    sensor, A practical approach toward embedded electronic prognostics,    Autotestcon 2007, IEEE Systems Readiness Technology Conference,    Baltimore's Inner Harbor, Baltimore Md., Sep. 17-20, 2007.

Disclosed in embodiments herein is a method for monitoring thehealth-state for electronic equipment, comprising: measuring current andvoltage at an input and an output of the electronic equipment andacquiring data therefrom; using the measured data, calculatingperformance metrics for the equipment; separating the measured data intoa plurality of data classes; generating performance models for at leastone data class; extracting diagnostic features from measured data valuesby comparing calculated performance metrics with the diagnostic models;and identifying the source and severity of a fault based upon thediagnostic features.

Also disclosed in embodiments herein is a prognostic health managementsystem for monitoring performance of an electronic system, comprising: aplurality of electronic circuit components, located in said electronicsystem, at least one component having a modeled operating state relativeto at least one feature and generating respective electrical signalsrepresentative of the feature pursuant to the component operation; adata collection memory for storing samples of said electrical signals;and a computer processor, responsive to said electrical signals and themodeled operating state, for performing data analysis relative to thefeature and detecting a variance in the operation of the component,wherein the processor further determines the health and/or remaininguseful life of the component and the electronic system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart depicting general operation of an electronicsystem prognostic health monitoring system;

FIGS. 2A-2B are flowcharts illustrating alternative monitoringmethodologies;

FIG. 3 is an illustrative example of a global positioning device thatprovides an embodiment for the disclosed system;

FIGS. 4-5 illustrate example printed circuit board layouts (e.g., frontand rear sides of a GPS receiver circuit board) of the globalpositioning system receiver depicted in FIG. 3;

FIG. 6 is an illustration of a damage accumulation model used inaccordance with an aspect of the disclosed system and method;

FIG. 7 is a graphical representation of the results of a Monte Carloanalysis across multiple trials;

FIG. 8 is a block diagram of an exemplary global positioning systemreceiver;

FIG. 9 is a skyplot of recorded signal-to-noise data;

FIG. 10A is a plot for a histogram of normalized signal-to-noise ratioand FIG. 10B is a density plot of signal-to-noise ratio versuselevation;

FIG. 11 is a histogram of normalized signal-to-noise ratio data;

FIG. 12 illustrates an exemplary format of a data packet;

FIG. 13 is an exemplary illustration of an accelerated failure testsetup;

FIGS. 14-15 are exemplary plots depicting the relationship of themeasured feature offset;

FIGS. 16 and 17 are exemplary graphical user-interface screensdepicting, respectively, healthy and degraded global positioning systemreceiver data in accordance with an aspect of the present invention;

FIGS. 18A and 18B are illustrative graphs of cyclic-redundancy checkerrors for master and slave units tested, and FIG. 18C illustrates anevaluation circuit employed in one embodiment;

FIGS. 19A and 19B are, respectively, graphs of the received signalstrength indicator versus distance for the master and slave devices;

FIGS. 20A and 20B are data plots that illustrate power sensitivity as afunction of frequency offset at a fixed data rate;

FIGS. 21A and 21B are illustrative plots that depict frequency offsetversus distance for master and slave devices, respectively;

FIGS. 22A and 22B are illustrative plots that depict health stateclassifications for master and slave devices, respectively;

FIG. 23 is a plot depicting the various modes (levels of functionaldegradation) using the relationship between amplitude and a normalizedfeature;

FIG. 24 is an illustration of an exemplary test system (MPC7447 μP on aremovable processor card);

FIGS. 25A and 25B are graphs illustrating exemplary data for anaccelerated aging process;

FIG. 26 is a graphical illustration of the use of a loss resistanceperformance metric for classification of performance;

FIGS. 27A and 27B are comparative examples of plots showing performancemodels generated using a neural network, and analytical model,respectively;

FIG. 28 is an illustrative schematic of an electric power converterhaving an embedded health monitoring system;

FIG. 29 is a block diagram of an exemplary embedded health monitoringsystem;

FIGS. 30 and 31 are block diagrams of an exemplary external healthmonitoring system;

FIG. 32 is a block diagram of a health monitoring unit as depicted inFIGS. 29 and 31;

FIG. 33 is a graphical representation of automated test equipment usedwith the health monitoring system;

FIG. 34 is a block diagram of the system of FIG. 33;

FIGS. 35A-D are graphical illustrations of loss resistancecharacterization curves for each of four power supplies; and

FIG. 36 is a block diagram illustrating an embodiment for a ringingcharacterization technique;

FIG. 37 is a representation of a ringing characterization in theembodiment of FIG. 37;

FIGS. 38 and 39 illustrate an exemplary test fixtures for insulated gatebipolar transistors;

FIG. 40 is an illustration of a thermoelectrical accelerated test;

FIG. 41 is a plot of the life expectancy;

FIGS. 42A-C depict graphical results of latching, and self-healing undera thermo-electrical accelerated test;

FIG. 43 is a series of graphical representations of changes in theringing characteristic of new vs. aged insulated gate bipolartransistors;

FIGS. 44A and B, illustrate a graphical representation of parameterdecays while aging;

FIGS. 45A and B are schematic illustrations of a platform for agingevaluation using ringing characterization;

FIGS. 46A-B are graphical representations of the ringing frequency;

FIG. 47 is a graphical depiction of attenuation of the ringing frequencyin insulated gate bipolar transistors;

FIG. 48 is schematic illustration of a model for ringing;

FIG. 49 is a schematic illustration of an exemplary Simulink model withaging;

FIGS. 50A-B respectively illustrate simulated results for the insulatedgate bipolar transistors aging model for healthy and aged transistors;

FIG. 51 is an illustrative representation of a modified test setup;

FIG. 52 is an illustration of an enhanced test platform havinginterchangeable power transistors;

FIGS. 53A-C graphically illustrate example results from the platform ofFIG. 52;

FIGS. 54A-C depict derivatives of the current signal in FIGS. 53A-C anddetail of the ringing during switching;

FIG. 55 illustrated, in a block diagram, a possible circuit for ringingcharacterization;

FIG. 56 is an illustration of the band-pass filter design requirementsfor a ringing diagnostic circuit; and

FIG. 57 is an example of a band-pass filter meeting the design of FIG.56.

DETAILED DESCRIPTION

The following detailed description includes an embodiment for a healthmonitoring system capable of generating a health assessment forelectronic systems. The disclosure is divided into three sections,including first a description of the general operation of the healthmonitoring system followed by detailed explanation of three embodimentsdemonstrating this capability.

General Operation

The general operation of the electronic system prognostic healthmonitoring system is set forth in the flowchart of FIG. 1. Each step isdescribed in more detail as follows. At step S1100, the process beginsby monitoring sensor values. There are several ways to monitor sensorvalues, which are dependent on the hardware architecture of themonitoring system as described in the next section. An outline of themonitoring procedure is provided in FIG. 2A. For the case where thehealth monitoring system is configured as an embedded or externalmonitoring system, step S1130 is used to monitor data by acquiringsensor values. Alternatively, when the health monitoring system isconfigured as automated test equipment (ATE), monitoring values areacquired by executing an automated test as described in FIG. 2B,beginning with step 1150. Both of these steps are now described infurther detail.

At S1130, sensor values are acquired from each sensor in the healthmonitoring unit using an analog-to-digital converter (ADC). The ADCdigitizes the output of each sensor and sends the results to aprocessor. Next, in step S1150, the ATE acquires monitoring data byperforming an automated test. Each automated test is executed using aprofile, or collection, of pre-determined operating points, or setpoints. Each set point is used to apply an electrical stimulus to theelectric power converter, or the device under test (DUT). The procedurefor the automated test is described relative to steps S1551-S1558 (FIG.2B). The appropriate profile for the DUT is selected from the disk driveor similar storage media or storage device and loaded into memory. Also,the first set point is sent to the digital controllers at the PowerSource (PS) and Loading and Measurement Module (LMM). A command is sentto the digital controllers at the PS and LMM to execute the set point.An appropriate amount of time (approximately on the order ofmilliseconds) is spent waiting for the source module and all of theloading modules of the PS and LMM to adjust to the new set point,respectively.

At step S1554, after the delay, the source module and the loading moduleare checked to verify each set point was reached to within the tolerancespecified by the profile. If the set point is not reached for at leastone of the loading modules or source module then step S1553 is repeated.Otherwise, step S1555 is executed. At step S1555 voltage and currentmeasurements made at the PS and loading and measurement module data aresent to the computer using the standard data bus and then stored intomemory. The profile is examined at step S1556 for additional set points.If more set points are available then step S1557 is executed, otherwisestep S558) is executed. The next set point is then sent to the digitalcontrols at the PS and LMM. At step S1558, all of the acquiredmeasurements made in step S1555 are saved to the disk along withinformation uniquely identifying the device, such as the serial number,model, and manufacturer.

After monitoring the data, performance metrics are calculated at stepS1200 using explicit analytical expressions in term of monitored datavalues. All calculations are made using a microprocessor, processor orembedded processor. Performance metrics are calculated for each acquiredset of monitored values.

Next, at step S1300, a fuzzy, neural network or fuzzy-neural network isused to separate the acquired monitored values and associatedperformance metrics into multiple data classes, or distinct groups.

Performance models are generated (step S1400) using performance metricsand monitoring values associated with each data class. A performancemodel may consist of an analytical best-fit expression or a neuralnetwork that relates the performance metrics with the monitoring valuesfor each data class. Other examples, include a comparison of twoperformance models generated using a neural network and analyticalmodel, respectively, with the measured performance metric lossresistance.

In the training of diagnostic models step S1500, the diagnostic modelsare used to represent characteristics, or features, of the electronicsystems' operational lifetime. This period is also referred to thenormal health-state. Diagnostic models are performance models generatedfrom a series of past or historical, monitoring values and performancemetrics to characterize, or baseline, the healthy or normal health-stateof the system. The diagnostic models may be updated periodically duringthe lifetime of the system and are stored in a non-volatile memory orrecorded in another medium for later access.

Diagnostic features are extracted (step S1600) from monitored values bycomparing measured performance history with the trained diagnostic modelfor each class using statistical analysis, trend analysis, thresholdanalysis, pattern analysis, quantitative state estimation, and signalprocessing techniques.

A health assessment is generated at step 1700, where a neural network,neural-fuzzy network, Bayesian network, causal network, rule-basedsystem, or an expert system is used to combine the diagnostic featuresinto a health assessment for the power converter. The health assessmentidentifies the source of the fault by isolating down to a component or agroup of components, probability of overall system failure, and anestimate of the remaining useful lifetime (RUL) [e.g., in units of timeor other time-related metric] of the system. The RUL is generated usingtrending analysis, quantitative state estimation techniques, andqualitative state estimation techniques.

Trained models, health assessments and RUL predictions are stored inrewritable non-volatile memory such as flash memory, hard disk drive andmagnetic memory as represented by S1800. Also, stored are historicalmonitoring values and associated performance metrics used to update thetrained diagnostic models in step S1500. At step S1900 the healthassessment is reported or sent to a third party hardware or softwaremodule either via a digital communication protocol or displayed to ahuman operator using visual indicators such as light-emitting diodes(LEDs) and electronic displays.

Methodology 1—Use of existing electronic systems data (Circuit as aSensor): The use of data and signals already present in a system for thepurposes of diagnostics and prognostics is a method of analysis with awide range of potential benefits and applications. This process is alsoreferred to as the “circuit-as-a-sensor”(CAS) approach. The objective ofthis approach is to analyze all available forms of information presentwithin the system already, and fuse together critical data relevant tothe health state of the device. After desired parameters (S1100) havebeen selected, the device is then analyzed in a variety of potentialoperating environments in order to determine ranges of variation on thecritical parameters previously selected. Monitoring of criticalparameters also occurs as the device progresses on the path from fullyoperational to device failure. During the monitoring and data collectionphase, the procedure implements device mode detection and analysistechniques to differentiate between useful device data and device datathat may be irrelevant due to undesirable device conditions. Once acomprehensive knowledge base is developed for all critical parameters,individual parameter models (S1400) can be generated and combined tocreate an overall device model. Once critical parameters have beenidentified and analyzed under a variety of possible operatingconditions, the information obtained through analysis can be combinedwith observed device operation to generate performance metrics used inclassifying (S1700) various operating stages of the device. At thisstage in the process, device models can be extracted and adjusted basedon simulation and experimental data. Once the device model has undergoneextensive simulated and experimental analysis, device health assessmentscan be accurately formed.

The Garmin GPS 15L-W, shown in FIG. 3 was selected for failure modeanalysis and accelerated failure testing. The Garmin GPS was selectedbecause of the thorough documentation, its commercial, off-the-shelf,availability and cost, it included embedded temperature sensingthermocouples and has a relatively small form factor.

Although it is to be understood that the disclosed embodiments havebroad and extensive applicability to non-GPS and non-avionic systems,the following background is useful in order to set the context in whichthe GPS embodiment was developed and tested. The global positioningsystem (GPS) is a space-based radio-navigation system managed by theU.S. Air Force (USAF). GPS, originally developed as a military forceenhancement system, supports the existence of two different services:the Precise Positioning Service (PPS) and the Standard PositioningService (SPS). The PPS is reserved for military use and requires specialPPS receivers to access the system, while the SPS is available tocivilian users throughout the world. Fundamentally, both servicesoperate on the same principles. Accuracy is the main difference betweenthe two systems; the SPS provides a less accurate positioning capabilitythan its counterpart; Global Positioning System Standard PositioningService Signal Specification, Department of Defense, 1993; herebyincorporated by reference in its entirety.

The GPS constellation includes twenty-four satellites in continuousoperation with six additional backup satellites, each having an orbitalradius of 26559.7 km. All satellites in the constellation are separatedinto six groups consisting of four satellites per group and areseparated 60° apart with a maximum angle of inclination of 55° from theequator. Additionally, the satellites are designed to provide reliableservice over a 7 to 10 year life time. Every active satellite broadcastsa navigation message based upon data periodically uploaded from theControl Segment (CS), which continuously monitors the reliability andaccuracy of each satellite. All GPS systems consist of three majorsubsystems: (i) GPS Satellites; (ii) Transmission Paths; and (iii) GPSReceivers

Failure Mode Analysis

A study of stand-alone GPS receivers that met Federal AviationAdministration TSO C-129 requirements found that the probability of areceiver outage from a software-related problem was much greater thanthe occurrence of a total device failure (see e.g., Nisner, P. D., andR. Johannessen: Ten Million Points From TSO Approved AviationNavigation: Journal of the Instate of Navication, Vol. 47, No. 1.Institute of Navigation, Fairfax, Va. (Spring 2000) 43-50; herebyincorporated by reference in its entirety). To explain this phenomenon,a physical understanding of GPS receiver failure is required.

Failure mode analysis, starting at the device level, is essential toshow that software failure modes manifest from small physical deviationsin high frequency analog circuits. In failure mode analysis, circuitmodels are developed to simulate a circuit's performance when damageaccumulates in discrete components. Monte Carlo simulation utilizesthese device-level circuit models to analyze the changes in performancecharacteristics of the high frequency analog circuits. Then asystem-level, fault-to-failure progression model is developed based onchanges in circuit performance characteristics. The identified featuresfrom the system-level model describe the fault-to-failure transition.

Component Identification

Identification of the critical components in the target application isrequired before any failure mode analysis is performed. A criticalcomponent is a discrete element, such as a single transistor, or arelatively complicated circuit, such as a radio-frequency (RF) mixer,that contains a relatively high probability (or risk) of failure. FIGS.4 and 5 present example board layouts of the GPS receiver investigated.Table 1 provides a summarized reference of critical components with anassociated number and color to identify the component name and circuittype.

TABLE 1 Component Reference Table Circuit Type Low High Low High Freq.Freq. Imped- Imped- No. Circuit or Device Analog Analog ance ance 1Antenna X 2 Low Noise Amplifier X 3 Bandpass Filter X 4 RF Mixers X 5Crystal Oscillator X 6 Digital Signal X Processor 7 Flash Memory X 8Serial Drive X 9 Serial Port X 10 Voltage Regulator X

Circuit Analysis

Many high frequency analog circuits, such as RF mixers and RF low noiseamplifiers (LNA), are implemented with MOSFET devices. These circuitsare sensitive to device variations at frequencies exceeding 1 GHz.Therefore, variation in any device, either active or passive, can causethe following circuit characteristics to change: (i) Phase response;(ii) Frequency response; (iii) Linearity; (iv) Gain; and (v) Impedancematching.

RF mixers are composed of transistors and traditional passive devicesincluding inductors, capacitors, and resistors. A Monte Carlo worst-caseanalysis was performed on a RF mixer circuit. The time-dependent dioxidebreakdown (TDDB) damage accumulation model, shown in FIG. 6, replacedthe MOSFET devices in both circuits. The equivalent gate-to-sourcecapacitance (C_(gso)) provided a damage accumulation parameter with atolerance of 10%. FIG. 7 shows the results of the Monte Carlo analysis.The time domain phase of the RF mixer plots for ten different trialsindicated a maximum phase difference between any two plots of about tenpercent.

System Analysis

Analyzing a sophisticated electronic system using a schematic can berather complex. Instead, a system diagram can be used to model systemfunctionality by representing the functionality of the electronicsystem. For example, FIG. 8 shows a block diagram of a GPS receiver 800.A GPS receiver 800 typically includes three fundamental stages: (i)Input stage 810; (ii) Conversion stage 820; (iii) Processing stage 830,as well as an antenna 840.

These stages are very interrelated because of the complex nature of theGPS receiver. The input stage is the first stage in any GPS receiver.The front end of the input stage 810 includes an antenna 840 and a RFamplifier. The conversion stage 820 demodulates the incoming RF signalfor data recovery. It includes the demodulator, phase-lock feedbackmechanism, and data recovery/reconstruction. In a basic binary phaseshift keying (BPSK) system, the output from the RF amplifier isdown-converted to a lower frequency or an intermediate frequency (IF)and mixed with quadrature local-oscillator (LO) signals. The compositesignal is then fed back to phase-lock to the carrier. Low pass filteringthe outputs of one of the mixers recovers the data as described byChenming Hu and Qiang Lu, “A unified gate oxide reliability model,” InIEEE International Reliability Physics Symposium, pages 47-51, 1999;which is hereby incorporated by reference in its entirety. The data canbe digitally processed once it is recovered from the RF signal. Thedigital processing stage recovers the navigation messages bycontinuously synchronizing each satellite's gold code with the incomingdata stream.

The overall reliability of a GPS receiver depends on the tolerance ofeach subsystem. The two largest reliability concerns include the lownoise amplifier (LNA) and the RF mixers. As shown earlier, changes inphase response, frequency response, impedance mismatching, and linearitywere all attributed to device-level degradation of MOSFET devices.Consequently, synchronizing errors occur when the digital processingstage decodes the incoming data stream. The end result is a reduction incoverage of the GPS receiver which triggers two typical failure modes:

-   -   Precision Failure—increased position error    -   Solution Failure—increased outage probability

These failure conditions result in measurable parameter changes duringfailure progression.

Failure Modes and Effects

Failure mode, effects, and criticality analysis, or “FMECA,” is a methodof analysis used to understand the root cause of failures, along withtheir relative probability of occurrence, criticality, and their effectson a system. The FMECA used for the GPS receiver in accordance with anembodiment described herein provided a complete description of thefault-to-failure progression.

Feature Extraction

A basic building block of the procedure is the selection and analysis ofthe features that will form the skeleton of the device model used forhealth assessment. A diagnostic feature is a system parameter (orderived system parameter) that is sensitive to the functionaldegradation of one or more circuits contained in the system. Diagnosticfeatures can be used to predict the occurrence of an undesired systemevent or failure mode. Direct measurements of diagnostic features aretypically not feasible because they require advanced and usuallyimpractical measuring techniques. However, system-level features canprovide valuable and easily obtainable diagnostic and prognosticinformation. For example, in a GPS receiver there are system-levelfeatures that are universal to every receiver. Most receivers reportthese features using the National Marine Electronics Association (NMEA)0183 protocol. Therefore, data acquisition techniques require only anRS232 connection from a computer to a receiver.

The system features are selected based on potential contribution tohealth state analysis, feasibility of extraction, and level of analysisrequired to produce meaningful information. Below is a list of criticalparameters in GPS systems that are used to make accurate heathassessments of such devices.

SNR—Signal-to-Noise-Ratio is a measure of the amount of actual signalpower divided by the total noise power present. This feature is readilyavailable and provided via the NMEA protocol, and can be interpreted viadetailed analysis to demonstrate relevance to health assessmentprocedures. The targeted SNR diagnostic feature was extracted from theskyplot (e.g., FIG. 9). A density plot of SNR vs. elevation angle wasgenerated as illustrated in FIG. 10A. The density plot was generatedassuming that SNR was independent of the azimuth angle. This assumptionis believed valid for measurements where the elevation angle is greaterthan 30°.

The SNR data used to generate the density plot fit provides a fourthorder system model. The fitting parameters (or coefficients) used in thefitting model were generated by taking the average values of the fittingparameters for twelve different data sets. The only degree of freedom inthe model is the SNR offset coefficient A_(o) of the fitting model inEquation 1. The SNR data was normalized using the fitting model, shownin FIG. 10B, showing a Gaussian distribution as illustrated in FIG. 11.The distribution of the normalized SNR data had a standard deviation ofapproximately 1.6.

SNR=A ₀+(0.4878)φ−(7.849×10⁻³)φ²+(5.710×10⁻⁵)φ³−(1.586×10⁻⁷)φ⁴  Equation1

BER—Bit-error-rate or bit-error-ratio is a parameter used to measure theaccuracy of a system subjected to the presence of electrical noise. Itis often calculated as the number of erroneous or incorrect bitsreceived divided by the total number of bits transmitted. All real worldelectronic systems dealing with transmission and reception of datathrough a realistic communication channel will be subjected to theeffects of electrical noise. BER was chosen as a critical parameter forits universal presence in digital communication schemes, ease ofcalculation/extraction from devices, as well as a proven capability toindicate trends relative to device health.

As mentioned previously, BER is ubiquitous is communication systems withdigital components, making it an ideal candidate for further analysisinto potential application in diagnostics and prognostics of electroniccommunication systems. Further analysis was then performed to determinethe ease of extraction of the BER information as well as healthindication capability. BER in GPS systems can be calculated throughtransmission of pseudorandom noise (PN) sequences or basically any typeof data that is known at transmission, and then the received informationis compared to the known transmitted data. This allows for a simple andaccurate method to obtain the BER of a system, qualifying it for anotherdesired characteristic of critical health parameters, ease ofextraction. Since BER is present in most GPS systems and can also beextracted fairly easily from GPS systems, it was then analyzed for itsperformance in health prediction. The last component of parameteranalysis was determined through successful simulation and hardwareexperimentation. Simulations were created and ran with variation ofparticular parameters implemented to simulate values that would beproduced by a degraded system. Particular parameters that were variedwithin the simulation included thermal noise, DC offset, phase offset,phase noise, and antenna gain. Monitoring the BER values of acommunication system as parameters were modified to simulate degradationof the system provided confirmation that BER is affected as the systemis degraded. Values were then taken from the simulations to generate arepresentative model for BER as a device progresses from healthy tofailure. Test bench experiments were then implemented with a GPS datasimulator and GPS receivers. This allowed the calculation of BER for anequivalent GPS system and the ability to analyze the accuracy of the BERmodel as generated from the simulation. A range of GPS receivers wereused, each exposed to a different level of degradation. The use of theGPS data simulator allowed prior knowledge of transmitted information,which is then compared to information obtained from the degradedreceiver units. Vast amount of BER data was produced, giving the abilityto fine tune the degradation model developed in software. The model isthen modified taking into considerations developed from the test benchexperiments. The end result is a refined model for BER in GPS systems,covering from a healthy device to failure. This resulting model is thenfused or integrated with similar or alternative models for othercritical parameters, providing the final device model.

CRC—Cyclic-redundancy-check is a fault detection method included in mostcommunication protocols that is used to determine if data that has beenreceived has been altered by noise during transmission. The CRC isessentially a mathematical function that takes as an input the data of aframe and a predetermined polynomial, dividing the frame data by thepolynomial, with the result producing a CRC value. The data at thetransmitter is appended with a CRC number which is verified by thereceiver—see FIG. 12 which illustrates an exemplary format of a datapacket. The CRC error reflects the number of packets that have amismatch between the CRC number at the receiver and transmitter.

As with BER, CRC is a very common parameter in most communicationprotocols, including those used in GPS, making it ideal forimplementation. The potential application of the CRC parameter was alsolooked into due to the ease of extraction. The CRC data is includedwithin the transmitted and received data (data packet format in FIG.12), making the process of extraction relatively easy as compared toother types of data. Analysis of CRC applicability to health assessmentwas performed through a detailed hardware experimentation usingmaster/slave combinations of healthy and degraded boards communicatingat various rates, frequencies, and distances.

LQI—Link Quality Indicator is a parameter that is included with the NMEAstandard commonly used for GPS devices. The Link Quality Indicator inmost GPS devices has a variety of potential values as shown below:

-   -   0—fix not available,    -   1—GPS fix,    -   2—Differential GPS fix    -   3=PPS fix    -   4=Real Time Kinematic    -   5=Float RTK    -   6=estimated (dead reckoning)    -   7=Manual input mode    -   8=Simulation mode        Although the information that could be gained from the LQI        parameter has relatively low resolution in terms of detail it        provides, the readily accessible nature of the parameter makes        it an ideal candidate to provide more information to compliment        the other critical parameters being observed.

RSSI—The Received Signal Strength Indication value is an estimate of thesignal level in the current channel. This value is based on the currentgain setting in the RX chain and the measured signal level in thechannel. Typical values for RSSI offset are presented in Table 2. Thisparameter is readily available as it is included in the NMEA standardand easily extractable as an integer value from the frame in which it iscontained. Similar range tests were conducted as with previousparameters, the goal being to observe the relationship between devicehealth and RSSI. Signal strength is an obvious choice for a criticalparameter as it has potential value as an indicator in degradation ofthe receiver circuitry, but also has potential to be a significantindicator in mode detection procedures.

TABLE 2 RSSI Offset Values Data rate RSSI 2.4 kbps 71 10 kbps 69 250kbps 72 500 kbps 72

FO—Frequency Offset refers to the offset between the transmittedfrequency and the received frequency. When using FSK, GFSK or MSKmodulation, the demodulator compensates for the offset between thetransmitter and receiver frequency, within certain limits, by estimatingthe centre of the received data. Previous studies have indicated thatanalog RF components are very sensitive to stress factors. It isestimated that minor changes in frequency synthesis capability willmanifest as changes in the frequency offset value.

A health state analysis technique that uses existing system data couldpotentially become unreliable or inaccurate if the system data used inparameter and device modeling becomes invalid. Invalid system data couldbe the result of a variety of events that would cause the system tooperate incorrectly, which in turn provides faulty data to the parameterand device models, resulting in inaccurate trending, false alarms,non-detection of failure, or a variety of other undesirable results. Thepurpose of mode detection is to verify that the device or system isoperating normally and therefore the data collected for device andsystem modeling will provide accurate and reliable predictions of healthstatus. Successful mode detection is accomplished through a combinationof real-time and historical analysis of available parameters containinginformation about the operating mode of the device.

Similar to the process of parameter selection mentioned above,parameters are carefully chosen that will provide existing, relative,and extractable information about the device mode. Parameters related tothe operational information of GPS devices can be determined through theuse of the available data sentences provided to conform to the NMEAprotocol. Relevant parameters are listed below:

LQI—This is the same parameter that is mentioned above in the healthassessment feature section. This parameter is one of the most basicpieces of information used in mode detection, as it describes whether ornot the device is able to obtain a fix on the satellites. As before, itsinclusion in the NMEA protocol provides that devices conforming to thepopular standard will have this information readily available.

NOS—Number of satellites in use is a parameter readily available in thedata sentences and is used in mode detection for GPS devices. In orderfor a GPS receiver to function properly, a minimum of four satellitesmust be in use to assure functionality.

DOP—Dilution of precision or other dilution of precision measurements(HDOP, VDOP, PDOP, and TDOP) is a measure of the confidence level in thedetermination of the receiver precision. The parameter is used in modedetection analysis for weighting the information received. When the DOPparameter is ideal at a value of 1, the information provided formodeling should be considered higher priority than information obtainedwhen the parameter is much higher.

GPS devices also posses capabilities to include proprietary sentencedata, which also provides valuable information in determining validmodes for system data collection and analysis. Beneficial Garminproprietary parameters are listed below:

Receiver Failure—This is a discrete value available to the device whichindicates whether the device is functioning or not.

ROM Checksum Test—Indicates if the device memory is functioningcorrectly.

Stored Data Lost—Indicates if the data has maintained information fromprevious operating instances.

RTC—Identifies the ability of the device to maintain a real-time clock.Since a real time clock is critical to GPS applications, a failure ofthe clock would result in device inoperability.

OD—Oscillator drift (OD) can be detected within the device and indicatedwithin a data sentence. Excessive oscillator drift would createerroneous results and poor data to modify models with.

Device Temperature—If the device is operating outside of its functionalspecification the device is not guaranteed to produce valid results.

Accelerated Failure Testing

Accelerated failure testing validated the derived diagnostic featureset. Accelerated failure testing is the process of determining thereliability of an electronic system over a short period of time byaccelerating environmental conditions as described by the MIL-STD-810specification [Silverman, Mike, “Summary of HALT and HASS Results at anAccelerated Reliability Test Center,” 1996; hereby incorporated byreference in its entirety]. The accelerated tests consisted of placing aGPS receiver (e.g., Garmin GPS 15L-W) under thermal cycling stress.During the test, the GPS receivers received a constant reference signalfrom a GPS satellite simulator located approximately six feet away. Alaptop monitored the features using a RS-232 connection. The laptop,including RS-232 buffer memory, and digital memory (RAM, disk orremovable media), stored the features (including principal feature) inmemory for analysis. In a real-time embodiment, such features may besimilarly stored and analyzed to track the performance of the devicebeing monitored. GPS testing was halted approximately every 100 cyclesto record live constellation data (DUT). The cycle time lasted aboutforty minutes for each test. FIG. 13 shows an exemplary setup for theaccelerated failure test.

Once all the desired critical parameters have been identified,experiments are performed in simulation and with actual devices togenerate valid parameter and device models, allowing for accurate systemhealth assessment. Computer software simulations are performed to obtaina general assessment of how the desired system is performing and howvarying parameters will affect system results. This allows actual deviceexperiments to be created and provides indications as to the type ofresults that should be expected from the actual device experiments.Although the simulations are created with real life parameters built into the simulation, it is impossible to perfectly replicate an actualfunctioning environment with computer software. Due to imperfectenvironmental matches between software simulations and real-worldenvironments, the results of the simulations and device experiments willdiffer. The information generated from software simulations form thebackbone of the models used to represent the system. Once hardwareexperiments are performed in the lab, the variations in results from thesimulated results are analyzed and hybrid models are formed out of anintegration or fusion of software and hardware models, with real-worldfactors accounted for within the model.

Electronic devices with both RF and digital components are becoming moreand more common as technology advances, resulting in a variety ofpossible software and hardware experiments available in the RF/digitalarea. GPS devices are based on high frequency communication betweensatellites in orbit and devices present on the earth's surface.Simulations of radio frequency links were performed to analyze thecommunication link present in GPS devices. The simulation reports avariety of parameter information such as bit-error-rate, signal power,and constellation data. As discussed previously, these parameters arecritical in health assessment and can be analyzed with ease. With aworking simulation and developed expectations as to how the systemshould perform in a real-world environment, actual hardware experimentscan then be performed to analyze differences in simulation and finalizemodels for maximum accuracy.

Experimental Results

GPS: Two Garmin GPS receivers were tested to failure. The first GPSreceiver (S/N 81417589) failed after approximately 500 cycles. Accordingto the test logs, the environmental chamber was set to cycle between−40° C. and 95° C. with a total cycling time of 40 minutes per cycle.FIG. 14 shows an exemplary illustration of the relationship of themeasured feature offset versus the number of applied thermal cycles,illustrating actual, predicted and best-fit information. Principlefeature offset was calculated with live constellation data. A solutionfailure occurred when the offset dropped below 30 dB. Therefore, thelast data point was extrapolated using the GPS Satellite Simulator. Thesecond GPS receiver (S/N 81417585) failed after approximately 450cycles. The environmental chamber was set to cycle between −40° C. and110° C. with a cycling time of about forty minutes. FIG. 15 shows theresults of the experiment run on the second receiver. The first fivedata points followed an exponential trend as represented in Equation 1.The amount of thermal cycling applied to the GPS receiver after thefifth measurement was determined by using the best fit line in FIG. 15.One thousand minutes (or 25 cycles) of additional accelerated failuretesting was necessary to achieve the targeted reduction in featureoffset value. The GPS receiver was then subjected to an additional onethousand minutes of thermal cycling and the offset calculated with liveconstellation data. Further degradation in the principle feature value(PFV) offset resulted.

The equation below calculated the predicted value of PFV offset.

PFV=A+Bexp(λN)  Equation 2

The best-fit parameters for each test are provided in Table 3 where A,B, and λ are experimental fitting parameters and N represents the numberof applied thermal cycles.

TABLE 3 Experimental Fitting Parameters Device Under Test FittingParameters (Eq 2) GPS Receiver (S/N 81417589) A = 38.53 [dB] B =−2.927e−004 [dB] λ = 2.1251e−002 GPS Receiver (S/N 81417585) A = 38.39[dB] B = −3.423e−006 [dB] λ = 3.2197e−002

The real-time health monitoring system utilized, in the describedembodiment, a MATLAB GUI as part of the experimental set-up. The systemused data from the FIG. 16, which shows the PHM results from a healthyGarmin GPS receiver and its associated health index. FIG. 17 shows thePHM results from a degraded Garmin GPS receiver and its associatedhealth index.

Radio-Frequency Integrated Circuit (RFIC): The CC2500 Radio FrequencyIntegrated Circuit (RFIC) single chip transceiver and SmartRF® 04Evaluation Board System (FIG. 18C) were selected for hardware testingand experimentation. A population of twelve (12) RFIC chips was used forthe failure testing. Seven of these chips were seeded with faults bymeans of accelerated aging. These chips were quantified to differentlevels of damage based on the time that they were subjected to elevatedtemperature conditions. The following table, Table 4, outlines thedegradation levels for the chips used in the study.

TABLE 4 Degradation levels of RFIC Damage level Chip No. Time (seconds)Healthy B5-B1 0 Level 1 S6 3300 S7 4200 S8 5100 Level 2 S9 6500 S10 6300Level 3 S11 6900 S12 7200

The results of the tests for the RFIC were analyzed to determine if CRChad a distinguishable relationship to the health of the unit. Theresults (see FIG. 18) indicated that CRC does have a noticeablerelationship to the health of the unit. FIGS. 18A and 18B show the CRCerrors for the Master and Slave Units. With the exception of one chip, aclear distinction can be observed between baseline and seeded fault testCRC data. Two chips exhibited complete communication failure but stillretained digital functionality. It was possible to configure and writeto the registers of the two chips.

FIG. 19 shows the trend of RSSI value of the chips with the varyingdistance. As expected, the RSSI value should decrease with increasingdistance. Four of the chips are shown for better visual clarity. Anoffset was also determined in relation to degradation levels.

Shown in FIGS. 20A and 20B are typical plots for power sensitivity as afunction of frequency offset at a fixed data rate. FIG. 20A shows thetypical sensitivity at 2.4 Kbps, when IF is 273.9 KHz, whereas FIG. 20Bshows typical sensitivity at 10 kbps, when IF is 273.9 KHz. FIGS. 21Aand 21B show the trend of frequency offset with the varying distance. Asseen, a characteristic separation (frequency offset) can be observedbetween the healthy and degraded chips.

Overall functionality was accessed by collectively analyzing the datagathered from the different test runs. The results from the range testswere used to construct a feature matrix (f) for each chip (Equation 3).Each row is assigned to one feature and captures the variation of thatfeature as a function of distance.

$\begin{matrix}{f = \begin{bmatrix}{RSSI}_{D\; 1} & {RSSI}_{D\; 2} & {RSSI}_{D\; 3} & \ldots \\{FreqOff}_{D\; 1} & {FreqOff}_{D\; 2} & {FreqOff}_{D\; 3} & \ldots \\{CRC}_{D\; 1} & {CRC}_{D\; 2} & {CRC}_{D\; 3} & \ldots\end{bmatrix}} & {{Equation}\mspace{20mu} 3}\end{matrix}$

Using Singular Value Decomposition (SVD), a multivariate classificationtechnique, the authors generated a fused health state from the featurematrix (f). In SVD, the matrix ‘f’ is represented as shown in Equation4.

f _(M×N) =U _(M×M)Σ_(M×N) V _(N×N) ^(T)  Equation 4

The first two elements of the principle diagonal of the Zigma matrix areplotted in 2D space (i.e. Σ_((1,1)) values along the x axis andΣ_((2,2)) values along the y axis).

As seen in FIGS. 22A and 22B, a distinct health state classification canbe observed in the Master and Slave. The healthy chips start out in atight cluster at bottom left of the plots and progress outward as theyare subjected to accelerated stress.

Methodology 2—Use of internal/existing system measurements to achieveelectronic system health assessment: The following disclosure shows theability to provide electronic system health assessment through existinganalog measurements, including: voltage, current, and temperature anddigital measurement: built-in self test (BIT or BIST) and relatedmeasurements.

The embodiments of this example take the form depicted in FIG. 1 andFIG. 2 and related subfigures. The monitored electronic system beginshealth assessment in step S1000 transitioning to S1100 where sensorsacquire the necessary analog and digital measurements. Application ofthis method is equally applicable to embedded monitoring, externalmonitoring, or ATE deployment; steps S1110, S1120, and S1130,respectfully.

In the ATE deployment, this method follows the process flow depicted inFIG. 2B. Entering the ATE testing regime in step S1150 following theexecution flow as described above. In this particular method, theloading and measurement module (LMM) may take the form of one or varioussoftware applications designed to load or test the system thoroughly. Itshould also be appreciated that similar functionality may be achievedusing customized hardware.

Upon completion of step S1100 (and sub steps), performance metrics arecalculated at step S1200 using the acquired analog and digitalmeasurements. Such measurements complement one another to calculateaccurate performance metrics. That is to say temperate may be used inconjunction with current and voltage to achieve normalization throughdissimilar temperature gradients. Digital measurements are used forstatistical merit adding verification to the calculated performancemetrics.

Once performance metrics are calculated, the system enters step S1300where classification techniques are applied. Specific techniques, asdescribed above, are applied to achieve mode detection discerningbetween levels of functional degradation (shown in FIG. 23 relative toan amplitude versus normalized feature plot). Up to four statisticalmoments (i.e. mean, variance, skew, and kurtosis) are used to classifythe functional operation of the electronic system.

The system used in one embodiment was the MPC7447 host processor, ahigh-performance, low-power 32-bit implementation of the PowerPC RISC(Reduced Instruction Set Computer) architecture with a full 128-bitimplementation of Freescale's AltiVec™ technology. A detailed search ofcommercially available products incorporating MPC7447 μPs on a removableprocessor card resulted in identification of Genesi's Pegasos PowerPCcomputing platform. The PegasosPPC utilizes a MPC7447 processor on anaffordable and completely removable edge card configuration, as shown inFIG. 24, which is inserted into a fully populated motherboard. Furtherinformation on MPC7447 can be found athttp://www.freescale.com/webapp/sps/site/prod_summary.jsp?code=MPC7447&srch=1#top.

In one scenario, thermo-electrical stress testing was used as aneffective third means of accelerated life testing yielding promptfailure. These included thermal cycling, thermo-electrical overstressand a combinational environment (Thermal Cycling and Vibration Stress).

The baseline measurements taken after successive thermo-electrical agingprocesses showed dramatic increases in current consumption. Each agingcycle escalated the core processor voltage causing a damaging, cascadedeffect of increasing core temperate causing increased currentconsumption. As the device is aged, the quantity of trapped electronsincreases causing the leakage current of the device to growproportionally at nominal operating conditions. This increased leakagecurrent is the primary indicator of incipient faults occurring withinthe device and has been measured and quantified over the acceleratedaging process, as shown in FIGS. 25A-B. More specifically, FIG. 25 Ashows the shift in feature mean as damage increases, and FIG. 25B showsthermo-electrical aging of the device.

Methodology 3 Performance assessment metrics derived from availableexternal measurements: A specific example demonstrating theaforementioned capability extends from the Navair Small BusinessInnovative Research (SBIR) program under contracts referenced above. Thefollowing discussion describes the ability to provide performanceassessment for electronic systems that are derived from availableexternal measurements.

The methodology once again follows the steps outlined in FIG. 1. Aftermonitoring the data, performance metrics are calculated at step S1200using explicit analytical expressions in term of monitored data values.All calculations are computed using a microprocessor, processor orembedded processor. Performance metrics are calculated for each acquiredset of monitored values, with primary performance metrics including lossresistance, power loss, and converter efficiency.

For example, power loss (P_(loss)) is computed by taking the differencebetween the input power and output power of an N output converter(Equation 5). Input power and output power is computed by multiplyingthe time-instantaneous values for current and voltage at each input andoutput port respectively.

$\begin{matrix}{P_{loss} = {P_{i\; n} - {\sum\limits_{i = 1}^{N}P_{{out}{(i)}}}}} & {{Equation}\mspace{20mu} 5}\end{matrix}$

Loss Resistance (R_(loss)) is computed by dividing, or normalizing, thecomputed power loss by the measured input current (I_(in)) squared(Equation 6).

$\begin{matrix}{R_{loss} = \frac{P_{loss}}{I_{i\; n}^{2}}} & {{Equation}\mspace{20mu} 6}\end{matrix}$

And, the Efficiency (Eff) is computed by taking the ratio of outputpower to input power of an N output converter as set forth in Equation7.

$\begin{matrix}{{Eff} = \frac{\sum\limits_{i = 1}^{N}P_{{out}{(1)}}}{P_{i\; n}}} & {{Equation}\mspace{20mu} 7}\end{matrix}$

Next, at step S1300, a fuzzy, neural network or fuzzy-neural network isused to separate the acquired monitored values and associatedperformance metrics into multiple data classes, or distinct groups. Anexample is illustrated in FIG. 26, where monitored values are separatedinto three different data classes (reference arrows 1, 2 and 3,respectively) using the performance metric loss resistance.

Performance models are then generated (step S1400) using performancemetrics and monitoring values associated with each data class. Anexample of an analytical best-fit model is provided in Equation 8relating the performance metric loss resistance with input voltage andcurrent. Provided in FIGS. 27A and 27B is a comparison of twoperformance models generated using a neural network and analytical modelrespectively with the measured performance metric loss resistance. Note:in the model, A and X are vectors, H₁₁ . . . H₃₃ are the modelingcoefficients, and the variables I_(in) and V_(in) represent the measuredinput current and output current, respectively.

R _(loss)(I _(in) ,V _(in))=A·X  Equation 8

where

A=[H₁₁H₁₂H₁₃H₂₁H₂₂H₂₃H₃₁H₃₂H₃₃]

X=[1 I_(in) ⁻¹ I_(in) ⁻² V_(in) V_(in)I_(in) ⁻¹ V_(in)I_(in) ⁻² V_(in) ²V_(in) ²I_(in) ⁻¹ V_(in) ²I_(in) ⁻²]^(T)

The steps S1700 to S1900 are applied after this stage.

Hardware Architecture

Three hardware configurations or embodiments for the electric powerconverter health monitoring system are described in this section, andinclude embedded, external, and automated test equipment (ATE). Embeddedhealth monitoring utilizes available hardware within the power converteritself. External health monitoring uses a third-party hardware moduleconnected external to the power converter to acquire monitoring valuesin a passive configuration. ATE health monitoring includes equipmentused to externally perturb the electric power converter with electricalstimuli (rather than passive monitoring) and measure the responses.

The embedded health monitoring system, illustrated in FIG. 28 utilizesavailable hardware within the power converter (E100) itself. As denotedby the figure, the embedded health monitoring system utilizes availablesensors to measure the input current and voltage from the input powerlines (E300) and all the output currents and voltages at the outputpower lines (E400) of the electric power converter (E100). The embeddedhealth monitoring system (E500) is also depicted in more detail as ablock diagram in FIG. 29. The embedded health monitoring system (B200)is contained within the electric power converter (B1000). The internalpower bus (B100) is connected by connection (B210) to the healthmonitoring unit (B220). The internal power bus (B100) includes inputpower lines (E300) and all the output power lines (E400). The digitalbus of the health monitoring unit (B230) is connected to the main databus (B300) of the electric power converter.

An external health monitoring system, for example the system illustratedin FIG. 30, can be implemented as a thirdparty module as previouslynoted. As denoted in the figure, the external health monitoring system(F300) is connected between the power source and load (F100) and theelectric power converter (F500) using two cables (F200) and (F400). Forthis embodiment all of the power and data lines of the input power cable(F200) and the converter power cable (F400) are wired together withinthe external health monitoring system (F300).

The external health monitoring system (C200) is described in more detailas a block diagram in FIG. 31. The input power cable (F200) contains theinput power lines (C110) and the data lines (C120) which are wiredtogether with the converter power lines (C310) and the converter datalines (C320) of the converter power cable (F400) respectively. Thisallows a direct connection between the power source and load (C100) andthe electric power converter (C300). Within the external healthmonitoring system (F300) there is a power bus (C210) and data bus (C230)which are connected to both the input power lines (C110) and input datalines (C120). Both the power bus (C210) and the data bus (C230) areconnected to the health monitoring unit (C220).

The health monitoring unit (A2000) used in the embedded healthmonitoring system (B220) and the external health monitoring system(C220) is depicted in the block diagram in FIG. 32. The power bus(A1000) contains the electrical connections delivering power to theelectrical power converter and the power supplied from the electricalpower converter. Contained within the health monitoring unit is acollection of censors (A100) and (A150). The sensors (A100) areconnected to the power bus (A105) to monitor the input and outputvoltages and currents of the electrical power converter where eachsensor is used to measure a single electrical quantity. Each sensor(A100) includes a transducer (A110) and low-pass filter (A120). Eachtransducer (A110) is used to convert either voltage or current to anappropriate electrical signal. The outputs of the low-pass filters fromeach sensor are connected to an analog-to-digital converter (ADC)(A200). The sensors (A150) are a collection of environmental sensors(A160). Each environmental sensor is essentially a transducer used tomeasure one or more of the following environmental parameters:temperature, vibration, humidity, radiation and pressure. Eachenvironmental sensor is connected to a low-pass filter (A120). Each lowpass filter (A120) is designed to anti-alias the electrical signalsmeasured from the transducer.

The output of each transducer is connected to one channel of the ADC.The ADC quantizes all of the sensor values into digital signals. The ADCis connected via connections (A250) to the embedded processor (A400).The embedded processor is connected to non-volatile memory (A300) tostore historical health assessment information, performance metrics, andtrained models. The health assessments generated by the processor can bedisplayed using visual indicators (A500) or sent to a third party usingthe data bus (A3000) connected to the embedded processor. Visualindicators include light-emitting diodes (LEDs), digital displays andsimilar devices. All of the power requirements for the health monitoringunit (A2000) are met using a low-power electric power converter (A600)connected at (A650) to the power bus (A1000).

The automated testing equipment (ATE) health monitoring system describedherein evaluates an electric power converter and generates a healthassessment. The ATE health monitoring system is similar to the embeddedhealth monitoring system and the external health monitoring system asdiscussed above, except that the monitored data is acquired by activelyperturbing the system with electrical stimuli rather than passivelymonitoring.

The ATE health monitoring system, illustrated in FIG. 33, can beimplemented as an offline health monitoring system for an electric powerconverter. As illustrated in the figure, the ATE health monitoringsystem (G1000) includes a power source (PS) (G100), loading andmeasurement module (LMM) (G200), DAQ computer (0300), standard data bus(SDB) (G400), the device under test (DUT) (G500), and the power bus (PB)(G600). The ATE health monitoring system (G1000) is described in moredetail referring to the block diagram of FIG. 34.

The power source (D100) provides electrical power to the DUT (D300)during a test. The power provided to the DUT is supplied by the sourcemodule (D140). The source module is a programmable power source able tochange voltage and current set points. The source module (D140) iscontrolled by the local digital controller (D130). The local digitalcontroller communicates with the standard data bus (D600) through theexternal bus interface (D120). The external bus, source module, anddigital controller are all connected together using a local data bus(D135). Using this interface, the digital controller transmits themeasured values of voltage and current from the source module (D140) tothe standard data bus (D600). The PS utilizes readily available electricpower sources (D110) such as single or three phase 115/230 VAC.

The loading module mainframe (D200) absorbs electrical power from theDUT during a test. The loading module mainframe contains at least oneloading module (D240) to absorb power from the device under test.Typically, there is one loading module for each output channel of thedevice under test. All the loading modules are, in one embodiment,controlled by the local digital controller (D230), although it isconceivable that a distributed control system may be employed. The localdigital controller communicates with the standard data bus (D600)through the external bus interface (D210). The external bus, digitalcontroller, and all the loading modules are connected together using alocal data bus (D235). Using this interface, the digital controllertransmits the measured values of voltage and current for all the loadingmodules (D240) to the standard data bus (D600). The LMM utilizes readilyavailable electric power sources (D220), such as single or three phase115/230 VAC.

The data acquisition (DAQ) system is designed to control the PS (D100)and LMM (D200) using an external bus interface (D410). The external businterface is connected (D405) to the standard serial bus (D600) and thecomputer (D420). The LMM also utilizes readily available electric powersources (D470), again for example single or three phase 115/230 VAC.

The computer (D420) is connected to a disk drive (D440), peripherals(D450), monitor display (D460) and memory (D470) through a local databus (D435). The computer includes of a microprocessor or embeddedprocessor. The disk drive (D440) is a non-volatile storage device thathosts the operating system, programs, measured data, test records, andtesting profiles. Alternative media may be employed in conjunction withor in place of the disk drive. The monitor display (D460) is used tovisually represent health assessment information to the operator. Also,memory (D470) provides temporary storage for programs, profiles, rawdata, and the operating system. Peripherals (D450) include devices theoperator uses to interface with the computer such as a mouse, keyboard,external data storage, wireless devices and other visual devices. Thecomputer (D420) is used to run, or execute, each automated test. Thecomputer controls the PS and LMM by sending commands to the SDB. Duringeach automated test, the computer also receives values measured from thePS source module and all the LMM load modules.

Four (4) unique power supplies were investigated to validate the lossresistance model developed above. FIG. 35 shows the loss resistancecharacterization curves obtained for each DUT.

Electronic PHM Development

Feature-based diagnostics and prognostics can be implemented forelectronic systems by identifying key prognostic features that correlatewith failure progression. Obtained features can be tracked and trendedover the system's life and compared with the model-baseduseful-life-remaining estimates to provide corroborative evidence of adegrading or failing condition. A feature-driven artificialintelligence-based approach can implement such a PHM system. Withexamples of good, bad, and unknown feature sets, classifiers can bedeveloped using an array of techniques from straightforward statisticalmethods to artificial intelligence methods such as neural networks andfuzzy logic systems. For a prognostics implementation, the automatedreasoning algorithm can be trained on evidenced features that progressthrough a failure. In such cases, the probability of failure, as definedby some measure of the “ground truth”, trains the predictive algorithmbased on the input features and desired output prediction. In the caseof a neural network, the network automatically (dynamically) adjusts itsweights and thresholds based on the relationships it sees between theprobability of failure curve and the correlated feature magnitudes.

Utilizing sound engineering principles and building on diligent study ofphysical failure mechanisms, the developed electronic prognostic healthmanagement technology leverages existing circuit operational data as abasis for prognostic feature extraction and provides a high-confidencecomponent health index. This index reflects the component's currentoperating condition and establishes the foundation for a prediction ofremaining useful life.

Disclosed in embodiments above is a method for prediction of electronicsystem failures and useful life remaining, comprising: selecting atleast one feature of the electronic system for monitoring, said featurebeing represented as a signal in the system; regularly monitoring thefeature and storing the signal in real-time without interrupting theoperation of the system; developing a model of the degradation of thesystem wherein the model includes the feature; and calculating, basedupon the model and the stored signals, the remaining useful life of thesystem.

Three major electronic system PHM methods have been identified and arebelieved to find particular use in avionic systems. The majorcharacteristics of multiple failure types were examined and techniquesidentified that are useful for monitoring and predicting failures. Inone of the embodiments, the selection of GPS circuits for testingpermits a substitution of economical test articles for destructivetesting and data collection. The availability of an existing data streampermits monitoring and implementation of prognostic algorithms withoutadditional sensors, an important aspect of the technique demonstration.At least one method was developed following a NMEA 0183 protocol tointerface a GPS Receiver required to perform the accelerated failuretests outlined herein. The extracted signals investigated during theaccelerated failure test provided a sound basis for feature extractionand statistical analysis.

This technique was extended to other RF electronic applications wheredigital data is readily available during the normal operation of thedevice, and it should be appreciated that the disclosed techniques mayfind similar applications. The RFIC results supplement the findings ofthe GPS effort and strengthen the circuit as a sensor methods. Thismethod can be extended to software defined radios and radar applicationsas well.

Using Methodology 2, as set forth above, the ability to detectdegradation using available external features was successfullydemonstrated. Also established was a distinct ability to capturefault-to-failure progression data through a series of accelerated agingtests designed to isolate and increase the likelihood of failure due tospecific known failure mechanisms. The matriculated failure modes werequantified through minimally invasive monitoring of system feature dataas the device degraded over time. The developed understanding ofsemiconductor device failure and the ability to measure and trend suchshifts in performance indicates the further ability to developprognostic health monitoring techniques for a wide breadth of digitalcomponents and systems.

has Also described is an ability to identify external available featuresas traceable indicators of damage accumulation. The switch mode powersupply (SMPS) use case, Methodology 3, demonstrated the capability ofusing these features to derive performance of critical electronicsystems.

Turning now to another exemplary embodiment, standard power drives foundin industry and military fleets are based on a power electronicscontroller and an induction motor. Recent trends indicate migration frominduction motors to synchronous permanent magnet (PM) machines or hybridtype motors. Nevertheless, the basic structure of the standardsix-transistor inverter feeding a PM or induction motor will most likelycontinue to be the basic inverter structure in the future. Standardinverters contain powerful microcontrollers and high-band signalinstrumentation devices with high-voltage isolation. These invertersmeasure real-time terminal voltage and current for each of the motorphases. In addition, inverters are designed to protect againstsimultaneous transistor leg trigger. Real-time measurements of input andoutput voltage are common when vector control techniques are required.Furthermore, many controllers include a tachometer for angular positionand speed feedback. Additionally, the inverter and motor haveover-heating protection typically located in the power transistor and inthe winding of the motor; in some cases, measures to protect the motorbearing against overheating are also incorporated into the design.Historically, frequency response has been one of the accepted methods ofcharacterizing semiconductors and electric machines, and off-lineimpulse tests (or stator surge) of electrical machines are among severaltechniques to determine turn faults in windings.

One ringing characterization technique, depicted in FIG. 36, has thepotential of measuring the relative aging effects of switchingtransistors, diodes, and stator motor windings. This technique takesadvantage of already available inverter instrumentation, used to measurecurrent and voltage, and the available microcontroller time of themodern power drives.

The relative aging effects of the switching transistors of the powerelectric drive are measured by characterizing the resonance frequency ofthe equivalent circuit. The equivalent circuit is formed by thecapacitance and inductance of the switching device in conjunction withthe inductance and resistance of the motor stator windings, which actsas a current source. Major contributors to the frequency response of thecircuit are the parasitic capacitances and inductances present in thesemiconductor and motor windings. Fundamentally, a strong relationshipexists between variation in semiconductor capacitances anddefects/errors in the fabrication process and winding aging.

During a transition between off-to-on states for a transistor such as aninsulated gate bipolar transistor, the drain-to-source properties may bemodeled as a switched capacitor for a short period of time. During thistransition, second and third order harmonic oscillations are observedamong the inductive load of the motor and the non-linear capacitivebehavior of the semiconductor. In FIG. 37, (a) illustrates a simplifiedmodel of the ringing oscillation observed during this transition betweenthe transistor (S1) and its clamping diode (S2). This is observed as astep response of a second order system. The circuit model of thephenomena includes the two switches (S1 & S2) and the stator-windingcoil of the circuit as shown in (b) of FIG. 37. In the same figure, (c)shows a simplified characterization of the system as a step response ofa second order circuit.

The RLC system illustrated in FIG. 37, section (b) can be expressed as asecond-order differential equation as shown below in Equation 9.

$\begin{matrix}{{{\frac{\partial^{2}{(t)}}{\partial t^{2}} + {2\xi \frac{\partial{(t)}}{\partial t}} + {\omega_{o}^{2}{(t)}}} = {0\mspace{14mu} {A/s^{2}}}}{{where}\text{:}}{\xi = {\frac{R}{2L}\mspace{14mu} {is}\mspace{14mu} a\mspace{14mu} {damping}\mspace{14mu} {factor}\mspace{14mu} {and}}}{\omega_{o} = {\frac{1}{\sqrt{LC}}\mspace{14mu} {the}\mspace{14mu} {resonant}\mspace{14mu} {frequency}\mspace{14mu} {{in}\mspace{14mu}\left\lbrack {{rad}\text{/}s} \right\rbrack}}}} & {{Equation}\mspace{20mu} 9}\end{matrix}$

The current modeled in Equation 9 has similar oscillatory behavior withthe ringing oscillation observed in FIG. 37 (a). The oscillatory, orringing, behavior can be computed, providing an opportunity to track theon-line values of the power device parameters, such as the dampingfactor, voltage over-shut, and ringing frequency.

$\begin{matrix}{{(t)} = {^{{- \xi}\; t}\left\lbrack {{A_{1}^{\sqrt{\xi^{2} - \omega_{0}^{2}}}} + {A_{1}^{\sqrt{\xi^{2} - \omega_{0}^{2}}}}} \right\rbrack}} & {{Equation}\mspace{20mu} 10}\end{matrix}$

When the damping factor is smaller than the resonant frequency, thesolution is complex creating a ringing oscillation. Thus,

$\begin{matrix}{{(t)} = {^{{- \xi}\; t}\left\lbrack {b\; ^{{j\omega}_{r}}} \right\rbrack}} & {{Equation}\mspace{20mu} 11}\end{matrix}$

where:

ω_(r)=√{square root over (ω₀ ²−ξ²)} is the ringing frequency in [rad/s]

The final expression (Equation 11) for the current represents the sameharmonic frequency of the voltage waveform shown earlier in (a) of FIG.37. Therefore, measuring this quantity can provide valuable informationfrom parameters such as the damping factor, voltage over-shut, andringing frequency, which may be used as precursors to failure. Thesingle second order system presented in FIG. 37 is a simplifiedrepresentation of the variable parameters that interact in a realsystem.

FIG. 38, depicts an embodiment in which a system and method for agingand modeling for an insulated gate bipolar transistors (IGBTs) isdeveloped and demonstrated. Accelerated failure testing was performed onInsulated Gate Bipolar Transistors (IGBTs) using the test-bed 3800 shownbelow in FIG. 38. As in the prior system, thermal-electrical stress wasused to generate device damage, applying a controlled temperature byswitching the transistor while reducing the heat transfer capability ofthe components. Additional information regarding monitored parameters,physical models, and preliminary results is provided in subsequentsections. The transistor's case temperature is controlled in a feedbackloop to ensure a gradual regulation of the aging process. The testidentified as thermo-electrical aging is the quickest and most effectivemethod to evoke degradation and failure in the device under testing.

The insulated gate bipolar transistor (Model: IRG4BC30KD), manufacturedby International Rectifier is an ultra-fast IGBT with ultra-fast softrecovery diode. Twenty four test boards, identical to those shown inFIG. 39, were fabricated to record transistor monitoring parameterswhile undergoing thermoelectric stress. The test-bed 3900 providesmeasurements of drain-to-source voltage and current (VDS and IDSrespectively) in order to compute the drain-to-source resistance (RDS).The temperature is measured along the front and back surfaces of thepower semiconductor to approximate the junction temperature of thedevice (T_(j)). In addition, the gate current (I_(G)) and the gatevoltage (V_(G)) are measured. The gate current is computed from thedifferential voltage over a 100K ohm resistance, for example. Thecurrent source used for this experiment was built from a modifiedvoltage controlled power supply. A diagram of the test setup is shown inFIG. 40. The feedback loop maintains temperature constant in order tokeep the aging process controlled. The semiconductor test-bed wasimplemented and developed using LabVIEW software. During operation, aseries of data files are created on the computing platform 4010 (e.g.,laptop, workstation, etc.) for each experimental measurement. All datafiles are saved in a standard file format for post analysis usingMATLAB.

Highly accelerated failure testing was performed on IGBTs. The IGBT(Model: IRG4BC30KD), manufactured by International Rectifier, is anultra-fast IGBT with ultra-fast soft recovery diode. Approximatelytwenty-four test boards, identical to those shown in FIG. 39, werefabricated to record transistor monitoring parameters while undergoingthermoelectric stress.

Thermo-Electrical Aging

FIG. 41 depicts a graphical characterization of the expected life withthermal and gate bias for a 50% accumulated failure rate. Lifeexpectancy given by the manufacturer's data of a power device, indicatedin FIG. 41 as a solid line 4110, can be extended for temperatures beyondthe maximum rated temperature, shown in dashed lines 4120, bythermo-electrical aging. The solid line is obtained from failure ratedata provided by the manufacturer; whereas the ultra-accelerated testproduces the experimental points on the dashed line. The dashed linecurve is adjusted using the aging model corresponding to hot carriereffect.

Latching was observed when thermal-electric stress was applied to theIGBT. Gate current, drain current, and case temperature for an IGBTundergoing thermal-electrical aging is shown in FIGS. 42A-C. A detaileddescription of the plots is provided below where descriptions for thepoints 1 through 4 correspond to FIGS. 42A-C accordingly. Before point1: The reference temperature is 205° C., the steady state operation is202° C. which corresponds to the IGBT drain current of 7 Amperes and noswitching is required to maintain the temperature under 205° C. At point1: A run off temperature was monitored triggering an automatic shutoffof the IGBT by cutting the gate voltage. However, due to latching, thetransistor cannot be successfully turned off. The temperature continuesto rise. At point 2: Manual interruption occurred. The temperature isdrastically reduced. At point 3: Manual connection is applied. The newreference is set to 180° C. At point 4: Switching occurs in order tomaintain the reference temperature. The operational characteristic ofthe device is maintained but aging and degradation in the deviceoccurred. Note: not shown, the degradation is observed as a reduction intime between latching.

Ringing Characterization

During a transition between an off-to-on state for a transistor, secondand third order harmonic oscillations are observed among the inductiveload and the non-linear capacitive behavior of the semiconductor. Asnoted previously, FIG. 37 (a) illustrates the ringing observed duringthis transition between the transistor (S1) and its clamping diode (S2).The circuital model of the circuit in FIG. 37 (b). From this model thesystem can be analyzed as a second order circuit.

IGBTs were aged by thermoelectrical stress until latching was observed.The transistors after the stress applied remained in good operationalcondition with not appreciable indication of aging in the staticparameter. FIG. 43 shows clear changes in the dynamic behavior of theswitching properties of the IGBT before (top) and after (bottom) aging.An appreciable increase in the damping (ξ) and large attenuation in theringing of high frequencies can be observed as well.

Considering FIGS. 44A and B, illustrated is a graphical depiction of howthe parameter decays while aging. The trigger is synchronized allowingswitching ringing comparison across different levels of agingtransistors. FIG. 44A shows how the main frequency increases with theaging. The rise in time delay is shown in FIG. 44B. Assuming no changesin the inductance of the simplified ringing model developed above, thepreliminary results based on this model suggest, according to theequations above and below, an increase in the resistance and a reductionin the capacitance of the parametric values in the power device. It willbe further appreciated that a direct relation among the most significantIGBT parameters and its changes due to the device aging may beinvestigated.

After a thermoelectrical stress is applied to the transistor, a ringingplatform is used to evaluate the changes in the parameters of thedevice. FIGS. 45A and B show, schematically, diagrams of this platformwere the load is the coil of a 3-phase induction motor. For theswitching frequency of 10 KHz the inductance coil becomes very large andacts as current source.

The ringing response was transformed using the inverse of an exponentialcurve fitted to the damping. This permitted obtaining the non-attenuatedfrequencies of the ringing as seen, for example, in FIGS. 46A and B.With the non-damped frequency response the attenuation of the mainfrequency component can be tracked. The graphs of FIG. 47 showattenuation in both of the main components with aging but a largeattenuation in the faster resonance frequency.

Modeling ODE and Circuital Representation

A circuital model, shown schematically in FIG. 48, describes in moredetail the ringing characterization observed from the experimentalresults presented above. Each element involved in the equivalent ringingcircuit, such as the diode and the IGBT, are modeled as elemental RLCcircuits. The electrical coupling between the diode and IGBT circuitalmodels are described using Kirchoffs equations as provided below inEquation 12

$\begin{matrix}\left\{ \begin{matrix}{V = {V_{T} + V_{D}}} \\{I = {I_{T} - I_{D}}}\end{matrix} \right. & {{Equation}\mspace{20mu} 12}\end{matrix}$

Each individual power element for the diode and the IGBT are describedusing the set equations below in Equation 13. The symbols VT and IDrefer to the voltage across the IGBT transistor aging model and thediode drain current.

$\begin{matrix}\left\{ \begin{matrix}{{V_{T}(t)} = {{R_{T}{i_{T}(t)}} + {L_{T}\frac{\partial{i_{T}(t)}}{\partial t}} + {\frac{1}{C_{T}}{\int{{i_{T}(t)}{t}}}}}} \\{{I_{D}(t)} = {\frac{v_{D}(t)}{R_{D}} + {C_{D}\frac{\partial{v_{D}(t)}}{\partial t}} + {\frac{1}{L_{D}}{\int{{v_{D}(t)}{t}}}}}}\end{matrix} \right. & {{Equation}\mspace{20mu} 13}\end{matrix}$

Next, the Laplace transfer functions for the IGBT transistor and diodemodels were obtained from the set of equations in Equation 13. Theresulting set of equations is provided below in Equation 14.

$\begin{matrix}\left\{ \begin{matrix}{{V_{T}(S)} = {{R_{T}{I_{T}(S)}} + {{SL}_{T}{I_{T}(S)}} + \frac{I_{T}(S)}{{SC}_{T}}}} \\{{I_{D}(t)} = {\frac{V_{D}(S)}{R_{D}} + {C_{D}{{SV}_{D}(S)}} + \frac{V_{D}(S)}{L_{D}S}}}\end{matrix} \right. & {{Equation}\mspace{20mu} 14}\end{matrix}$

An additional set of equations were derived for the voltage across thediode and current through the IGBT, represented as VD and ITrespectively, in terms of impedance of the diode and admittance of theIGBT expressed in Equation 15.

$\begin{matrix}\left\{ \begin{matrix}{{V_{D}(S)} = {{Z_{D}(S)}{I_{D}(S)}}} \\{{I_{T}(S)} = {{Y_{T}(S)}{V_{T}(S)}}}\end{matrix} \right. & {{Equation}\mspace{20mu} 15}\end{matrix}$

The admittance of the transistor and impedance of the diode were foundby solving the set of equations given in Equation 14 and 15. Theresulting expressions are provided below in Equation 16. Both sets ofequations are used to model the IGBT and diode in Simulink, shown inFIG. 49.

$\begin{matrix}\left\{ \begin{matrix}{{Y_{T}(S)} = \frac{C_{T}S}{{C_{T}L_{T}S^{2}} + {R_{T}C_{T}S} + 1}} \\{{Z_{D}(S)} = \frac{L_{D}S}{{C_{D}L_{D}S^{2}} + {R_{D}L_{D}S} + 1}}\end{matrix} \right. & {{Equation}\mspace{20mu} 16}\end{matrix}$

Finally, the IGBT aging was modeled as a dynamic process, where thelargest source of aging occurs in the transient stages represented as anRC circuit in FIG. 48. The same technique is used for modeling capacitorfor varying degrees of degradation. The basic model consists of a set offirst order differential equations that represent the RC circuit. Theset of Equations describing this model are presented below in Equation17 where V_(An) and I_(An) represent the current and voltage measuredacross the RC network.

$\begin{matrix}\left\{ \begin{matrix}{{V_{An}(t)} = {{R_{An}{i_{An}(t)}} + {\frac{1}{C_{An}}{\int{{i_{An}(t)}{t}}}}}} \\{{I_{An}(S)} = {{V_{An}(S)}\frac{C_{An}S}{{R_{An}C_{An}S} + 1}}}\end{matrix} \right. & {{Equation}\mspace{20mu} 17}\end{matrix}$

The transfer functions derived in Equations 16 and 17 were used todevelop a Simulink model for the circuital model shown earlier in FIG.48. The resulting Simulink model is represented in FIG. 49. Simulationof the IGBT aging model in Simulink indicates the disappearance of the 5MHz ringing frequency for the aged transistors. This is illustratedbelow in FIGS. 50A-B, where the 5 MHz frequency content is present onthe healthy transistor (FIG. 50A) and not the aged transistor (FIG.50B). Additionally, the ringing waveforms generated by the modelcorrespond to the experimental data shown earlier in FIG. 49 for ahealthy and aged IGBT.

Ringing Characterization in a Real System

The Microchip microcontroller and power inverter test-bed was enhancedto evaluate IGBTs by utilizing interchangeable transistor test boards,for example those shown in FIG. 51. Each of the six bridge IGBTs wereremoved from the inverter board and remounted onto a single transistortest board (see FIG. 39) and attached to a heat sink. The thermaldissipation of the heat sink was rated at 2° C./W allowing for nominalinverter operation (see FIG. 41). This enhanced platform withinterchangeable power transistors or (PIPT-1) allows for testing of oneor more aged IGBT transistors during real-time operation. Anillustration of the PIPT-1 testing platform is provided in FIG. 52.

The main characteristics of the PIPT-1 are summarized as follows: (i)perform simultaneous aging and evaluation of degraded IGBTs in areal-time system; (ii) open interface with standardized connectorsallowing for rapid development of the proposed fault diagnostic circuit;and (iii) reduce development time by preserving the capacitor bank,sensors, isolation unit, and control unit from the original power-drivetest bed. The results of ringing characterization applied to thetest-bed of FIG. 52 are shown in FIGS. 53A-C. FIG. 53A illustrates thecurrent for motor phases R and Y. A detailed snapshot of the current formotor phases R and Y shows the ringing oscillation during transistorturn-on and turn-off states. FIG. 53B shows the frequency spectrumallowing the characterization of the ringing with three frequencies.

By calculating the derivative of the current signal, the current biasmay be eliminated from the ringing, leading to a bettercharacterization. FIGS. 54A-C depict derivatives of the current signaland detail of the ringing during switching, and show how the dynamics ofthe ringing is almost identical for the two phases. A diagram of acircuit that can provide the ringing characterization feature for thediagnostic and prognostic module is presented in FIG. 55.

Aging Diagnostic Circuit

To distinguish between a healthy and aged transistor a tuned ringingfrequency system will be used to detect the 5 MHz frequency component.The 5 MHz ringing component was identified previously as a diagnosticfeature for the IRG4BC30KD IGBT transistor. Note: the primary feature of5 MHz will differ for different transistors. FIG. 56 shows theconceptual design of the ringing diagnostic circuit developed, includingthe filter design specifications. From the specification requirementsgiven FIG. 56, a band-pass filter can be designed using an operationalamplifier, for example the configuration and components depicted FIG.57.

It will be appreciated that various of the above-disclosed embodimentsand other features and functions, or alternatives thereof, may bedesirably combined into many other different systems or applications.Also, various presently unforeseen or unanticipated alternatives,modifications, variations or improvements therein may be subsequentlymade by those skilled in the art which are also intended to beencompassed by the following claims.

1. A method for monitoring the health-state for electronic equipment,comprising: measuring current and voltage at an input and an output ofthe electronic equipment and acquiring data therefrom; using themeasured data, calculating performance metrics for the equipment;separating the measured data into a plurality of data classes;generating performance models for at least one data class; extractingdiagnostic features from measured data values by comparing calculatedperformance metrics with the diagnostic models; and identifying thesource and severity of a fault based upon the diagnostic features. 2.The method according to claim 1, wherein extracting diagnostic featuresincludes statistical analysis.
 3. The method according to claim 1,wherein extracting diagnostic features includes trend analysis.
 4. Themethod according to claim 1, wherein extracting diagnostic featuresincludes threshold analysis.
 5. The method according to claim 1, whereinextracting diagnostic features includes pattern analysis.
 6. The methodaccording to claim 1, wherein extracting diagnostic features includesquantitative state estimation.
 7. The method according to claim 1,wherein extracting diagnostic features includes signal processing. 8.The method according to claim 1, further including displaying the healthassessment to an operator.
 9. The method according to claim 1, whereinsaid electronic equipment is an electric power converter used to convertpower from one form to at least another form.
 10. The method accordingto claim 1, wherein calculating performance metrics includes performingcalculations using explicit analytical expressions in terms of measureddata values.
 11. The method according to claim 1, wherein theperformance metrics include loss resistance, power loss, and converterefficiency.
 12. The method according to claim 1, wherein generatingperformance models includes using an analytical best-fit expression thatrelates the performance metrics with the monitoring values for each dataclass.
 13. The method according to claim 1, wherein generatingperformance models includes using a neural network that relates theperformance metrics with the monitoring values for each data class. 14.A method for employing a circuit having non-diagnostic functionality asa sensor, comprising: collecting, from the circuit, at least a first setof data relating to the non-diagnostic functionality when the circuit isoperating in a fault-free mode; placing the circuit in an extremeenvironmental condition and collecting, from the circuit, at least asecond set of data relating to the non-diagnostic functionality when thecircuit is operating under the extreme environmental condition; usingsaid first and second sets of data, modeling the operating state of atleast one component in said circuit; wherein the modeled operating stateis relative to the non-diagnostic functionality; and during operation ofthe circuit, generating respective electrical signals representative ofthe non-diagnostic functionality pursuant to the modeled operatingstate, and using the modeled operating state analyzing the electricalsignals representative of the non-diagnostic functionality to identifywhen operation of the circuit has degraded.
 15. The method according toclaim 14, wherein said circuit includes a plurality of electroniccircuit components (e.g., MOSFET).
 16. The method according to claim 14,wherein the circuit includes a plurality of components and where amodeled operating state is created relative to at least onenon-diagnostic function for at least two of the components in thecircuit.
 17. The method according to claim 14, wherein the circuitincludes digital components.
 18. The method according to claim 14,wherein the circuit includes radio-frequency components.
 19. The methodaccording to claim 14, wherein operations of collecting first and secondsets of data further include collecting a plurality of sets of dataunder fault-free and extreme conditions.
 20. The method according toclaim 14, wherein operations of collecting first and second sets of datafurther include collecting a plurality of sets of data field failuredata during normal circuit operation.
 21. The method according to claim14, wherein the circuit includes a radio-frequency component, and wheresaid data relating to the non-diagnostic functionality includes at leastone parameter selected from the group consisting of:signal-to-noise-ratio, bit-error-rate, cyclic-redundancy-check, linkquality indicator, received signal strength indication, and frequencyoffset.
 22. The method according to claim 14, wherein the circuitincludes a radio-frequency component of a global positioning system, andwhere said data relating to the non-diagnostic functionality includes atleast one parameter selected from the group consisting of:signal-to-noise-ratio, bit-error-rate, cyclic-redundancy-check, linkquality indicator, received signal strength indication, frequencyoffset, number of satellites, dilution of precision,
 23. A method forprediction of electronic system failures and useful life remaining,comprising: selecting at least one feature of the electronic system formonitoring, said feature being represented as a signal in the system;regularly monitoring the feature and storing the signal in real-timewithout interrupting the operation of the system; developing a model ofthe degradation of the system wherein the model includes the feature;and calculating, based upon the model and the stored signals, theremaining useful life of the system.
 24. The method of claim 23 whereinthe electronic system is a global positioning system and the at leastone feature includes a signal-to-noise ratio (SNR).
 25. The method ofclaim 23 further comprising at least one feature being communicated to acomputer via a standard interface, wherein the computer then employs themodel to trend deviations in the at least one feature and provide healthstatus information.
 26. The method of claim 23, wherein the model isdeveloped using a Monte Carlo worst-case analysis was performed on thesystem.
 27. A prognostic health management system for monitoringperformance of an electronic system, comprising: a plurality ofelectronic circuit components, located in said electronic system, atleast one component having a modeled operating state relative to atleast one feature and generating respective electrical signalsrepresentative of the feature pursuant to the component operation; adata collection memory for storing samples of said electrical signals;and a computer processor, responsive to said electrical signals and themodeled operating state, for performing data analysis relative to thefeature and detecting a variance in the operation of the component,wherein the processor further determines the health and/or remaininguseful life of the component and the electronic system.
 28. The systemof claim 27, further comprising a means for displaying the status of thecomponent.
 29. The system of claim 27, wherein the electronic system isan avionic system.
 30. The system of claim 27, wherein the electronicsystem is a navigational system.
 31. The system of claim 27, wherein theelectronic system is a radio-frequency system.
 32. The system of claim27, wherein the electronic system includes digital circuitry.
 33. Amethod for health monitoring and performance measurement of anelectronic system, including: during operation of the system, generatingelectrical signals representative of non-diagnostic functionalitypursuant to a modeled operating state for the system; using the modeledoperating state, analyzing the electrical signals from thenon-diagnostic functionality to identify when operation of the systemhas degraded; during operation of the system, generating signalsrepresentative of diagnostic functionality pursuant to a modeledoperating state for the system; and using the modeled operating state,analyzing the electrical signals from the diagnostic functionality toidentify when operation of the system has degraded.
 34. The methodaccording to claim 33, wherein the electronic system is a powerconverter and wherein the signals representative of diagnosticfunctionality include the frequency response of the circuit.
 35. Themethod according to claim 34, wherein said frequency response includesat least second order harmonic oscillations.
 36. The method according toclaim 33, wherein the modeled operating state includes a modelrepresenting a transistor as a switched capacitor.
 37. The methodaccording to claim 33, wherein the modeled operating state is a model ofthe ringing oscillation observed during a transistor transition.
 38. Themethod according to claim 37, wherein the modeled operating statefurther includes an aging component.